Article Figures & Data
Figures
- FIGURE 1.
Hierarchy of AI.
- FIGURE 2.
Validation phase of ANN demonstrates basic structure of ML-based ANN.
- FIGURE 3.
Basic structure of CNN, in which network extracts radiomic features, produces convolution function, pools data through kernel, and flattens pooled feature map for input into fully connected hidden layers of neural network. ReLU = rectified linear unit.
- FIGURE 4.
Schematic representation of semantic evaluation of imaging data, addition of radiomic feature extraction, and ANN analysis to produce small data and potential to integrate with big data to enhance outcomes and drive precision nuclear medicine.
- FIGURE 5.
Prediction of obstructive coronary artery disease with integration of DL outputs into polar maps. Image provides example of how outputs of AI might be integrated into traditional image display, in this case in form of polar maps with AI predictive data displayed in same mode. CAD = coronary artery disease; LAD = left anterior descending coronary artery; TPD = total perfusion defect. (Reprinted from (15).)
- FIGURE 6.
Model for potentially using CNN for improved pseudo-CT attenuation correction in PET/MRI (25) or for attenuation correction of PET without CT (or MRI) (26).
- FIGURE 7.
Several models for integration of AI into radiology have been proposed (4), but in nuclear medicine, perhaps most appropriate model captures best of each domain.
